Bioacoustic methods for biodiversity monitoring face limitations, particularly when relying on single-channel recordings, that lack spatial context. To address this, we tested a novel approach that incorporates spatial directivity into biological sound classification using a network of microphone arrays. Each array, comprising of four microphones, estimates the Direction Of sound Arrival (DOA), attributing spatial features to sound events. By combining these spatial features with conventional acoustic features, in combination with unconventional use of Kmeans clustering, we developed a methodology for classifying biological sound events. Our approach employs iterative adjustment of the number of clusters to refine classification, filter out noise (e.g., traffic), and reveal treelike class structures that separate between species and even individuals within same species. Experimental data from a multi-day field recording using synchronized 8-channel microphone systems demonstrates the method’s ability to separate sound events and identify individual organisms. The results highlight the potential of spatially informed acoustic analysis to overcome some limitations of bioacousticaly based diversity assessment.